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🔒 Password Attacks

Crack passwords and create wordlists.

John the Ripper (https://github.com/magnumripper/JohnTheRipper)
C
Linux/Windows/macOS
John the Ripper is a fast password cracker.

hashcat (https://github.com/hashcat/hashcat)
C
Linux/Windows/macOS
World's fastest and most advanced password recovery utility.

Hydra (https://github.com/vanhauser-thc/thc-hydra)
C
Linux/Windows/macOS
Parallelized login cracker which supports numerous protocols to attack.

Zero Trust Hackers (https://t.me/zerotrusthackers)
Tg
Linux/Windows/macOS/Mobile
Shares with you daily resources in the Cyber Security EcoSystem.

ophcrack (https://gitlab.com/objectifsecurite/ophcrack)
C++
Linux/Windows/macOS
Windows password cracker based on rainbow tables.

Ncrack (https://github.com/nmap/ncrack)
C
Linux/Windows/macOS
High-speed network authentication cracking tool.

WGen (https://github.com/agusmakmun/Python-Wordlist-Generator)
Python
Linux/Windows/macOS
Create awesome wordlists with Python.

SSH Auditor (https://github.com/ncsa/ssh-auditor)
Go
Linux/macOS
The best way to scan for weak ssh passwords on your network.

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Introduction to Python Programming
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Developing Android Apps with Kotlin
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Intro to JavaScript
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Intro to Machine Learning
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Free PHP Courses for Web Developer
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16 Websites for Coders 📍

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10. Nodejs :- https://lnkd.in/dwry8BKH

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12. Django :- https://lnkd.in/dYWQKZnT

13. PHP- https://quickref.me/php

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How AI Search Actually Works 👆

Tableau Cheat Sheet ✅

This Tableau cheatsheet is designed to be your quick reference guide for data visualization and analysis using Tableau. Whether you’re a beginner learning the basics or an experienced user looking for a handy resource, this cheatsheet covers essential topics.

1. Connecting to Data
- Use Connect pane to connect to various data sources (Excel, SQL Server, Text files, etc.).

2. Data Preparation
- Data Interpreter: Clean data automatically using the Data Interpreter.
- Join Data: Combine data from multiple tables using joins (Inner, Left, Right, Outer).
- Union Data: Stack data from multiple tables with the same structure.

3. Creating Views
- Drag & Drop: Drag fields from the Data pane onto Rows, Columns, or Marks to create visualizations.
- Show Me: Use the Show Me panel to select different visualization types.

4. Types of Visualizations
- Bar Chart: Compare values across categories.
- Line Chart: Display trends over time.
- Pie Chart: Show proportions of a whole (use sparingly).
- Map: Visualize geographic data.
- Scatter Plot: Show relationships between two variables.

5. Filters
- Dimension Filters: Filter data based on categorical values.
- Measure Filters: Filter data based on numerical values.
- Context Filters: Set a context for other filters to improve performance.

6. Calculated Fields
- Create calculated fields to derive new data:
- Example: Sales Growth = SUM([Sales]) - SUM([Previous Sales])

7. Parameters
- Use parameters to allow user input and control measures dynamically.

8. Formatting
- Format fonts, colors, borders, and lines using the Format pane for better visual appeal.

9. Dashboards
- Combine multiple sheets into a dashboard using the Dashboard tab.
- Use dashboard actions (filter, highlight, URL) to create interactivity.

10. Story Points
- Create a story to guide users through insights with narrative and visualizations.

11. Publishing & Sharing
- Publish dashboards to Tableau Server or Tableau Online for sharing and collaboration.

12. Export Options
- Export to PDF or image for offline use.

13. Keyboard Shortcuts
- Show/Hide Sidebar: Ctrl+Alt+T
- Duplicate Sheet: Ctrl + D
- Undo: Ctrl + Z
- Redo: Ctrl + Y

14. Performance Optimization
- Use extracts instead of live connections for faster performance.
- Optimize calculations and filters to improve dashboard loading times.

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Master Data Science & Machine Learning

1️⃣ Basics of Data Science

🏷 Statistics and Probability
◾️ Statistics & Probability (https://www.khanacademy.org/math/statistics-probability)
🏷 Linear Algebra
◽️ Essence of Linear Algebra
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🏷 Python
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🏷 Unlimited Resources
◽️ https://t.me/pythonresourcestp
3️⃣ Data Analysis and Manipulation
🏷 Pandas Library
◾️ Pandas Documentation
(https://pandas.pydata.org/pandas-docs/stable/)🏷 Data Preparation with Pandas
◾️ Data Wrangling with Pandas
(https://realpython.com/pandas-dataframe/)🏷 NumPy Library
◾️ NumPy Documentation
(https://numpy.org/doc/stable/)4️⃣ Data Visualization
🏷 Matplotlib and Seaborn Library
◾️ Matplotlib (https://matplotlib.org/stable/users/index.html) / Seaborn (https://seaborn.pydata.org/)
🏷Tableau Public Platform
◾️ Tableau Public
(https://public.tableau.com/app/discover)5️⃣ Principles of Machine Learning
🏷 scikit-learn Library
◾️ scikit-learn
(https://scikit-learn.org/stable/index.html)
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6️⃣ Learning Algorithms
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🏷 Unlimited Resources
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7️⃣ Deep Learning
🏷 TensorFlow Library
◾️ TensorFlow Tutorial
(https://www.tensorflow.org/tutorials)
🏷 PyTorch Library
◾️ PyTorch Documentation
(https://pytorch.org/docs/stable/index.html)
🏷 Unlimited Resources
◽️ https://t.me/mlresourcestp
8️⃣ Big Data Technologies
🏷 Spark Framework Course
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(https://www.youtube.com/watch?v=S2MUhGA3lEw)
🏷 Unlimited Resources
◽️ https://t.me/datascienceresourcestp
9️⃣ Advanced Topics
🏷 Natural Language Processing Course in Python
◾️ NLP in Python
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🏷 Unlimited Resources
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1️⃣ Share Your Projects on Kaggle and GitHub
🏷 Kaggle Platform
◾️ Kaggle (https://www.kaggle.com/)
🏷 GitHub Platform
◾️ GitHub (https://github.com/)

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Best Resources to learn Data Science 👇👇

Python Tutorial (http://pythontutorial.net/)

Data Science Course (http://kaggle.com/learn) by Kaggle

Machine Learning Course (http://developers.google.com/machine-learning/crash-course) by Google

Best Data Science & Machine Learning Resources (https://topmate.io/learning_resources/1406977)

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Python Interview Resources (https://t.me/pythonresourcestp/40)

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Java Summary

🔰List of Decompilers🔰

JVM-Based Languages
• Krakatau (https://github.com/Storyyeller/Krakatau) - the best decompiler I have used. Is able to decompile apps written in Scala and Kotlin into Java code. JD-GUI and Luyten have failed to do it fully.

• JD-GUI (https://github.com/java-decompiler/jd-gui)

• procyon (https://bitbucket.org/mstrobel/procyon/wiki/Java%20Decompiler)

◦ Luyten (https://github.com/deathmarine/Luyten) - one of the best, though a bit slow, hangs on some binaries and not very well maintained.

• JAD (http://varaneckas.com/jad/) - JAD Java Decompiler (closed-source, unmaintained)

• JADX (https://github.com/skylot/jadx) - a decompiler for Android apps. Not related to JAD.

.NET-Based Languages
◦ dotPeek (https://www.jetbrains.com/decompiler/) - a free-of-charge .NET decompiler from JetBrains

◦ ILSpy (https://github.com/icsharpcode/ILSpy/) - an open-source .NET assembly browser and decompiler

◦ dnSpy (https://github.com/0xd4d/dnSpy) - .NET assembly editor, decompiler, and debugger

Native Code
◦ Hopper (https://www.hopperapp.com/) - A OS X and Linux Disassembler/Decompiler for 32/64-bit Windows/Mac/Linux/iOS executables.

◦ cutter (https://github.com/radareorg/cutter) - a decompiler based on radare2.

◦ retdec (https://github.com/avast-tl/retdec)

◦ snowman (https://github.com/yegord/snowman)

◦ Hex-Rays (https://www.hex-rays.com/products/decompiler/)

Python
◦ uncompyle6 (https://github.com/rocky/python-uncompyle6) - decompiler for the over 20 releases and 20 years of CPython.

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ML Crash Course by Google
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Important Coding Concepts 👆

Practice these 5 intermediate SQL interview questions today!

1. Write a SQL query for cumulative sum of salary of each employee from Jan to July. (Column name – Emp_id, Month, Salary).

2. Write a SQL query to display year on year growth for each product. (Column name – transaction_id, Product_id, transaction_date, spend). Output will have year, product_id & yoy_growth.

3. Write a SQL query to find the numbers which consecutively occurs 3 times. (Column name – id, numbers)

4. Write a SQL query to find the days when temperature was higher than its previous dates. (Column name – Days, Temp)

5. Write a SQL query to find the nth highest salary from the table emp. (Column name – id, salary)

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